Convolutional Neural Networks¶
Project: Write an Algorithm for a Dog Identification App¶
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.
Why We're Here¶
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead¶
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Write your Algorithm
- Step 6: Test Your Algorithm
Step 0: Import Datasets¶
Make sure that you've downloaded the required human and dog datasets:
Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location
/dogImages.Download the human dataset. Unzip the folder and place it in the home directory, at location
/lfw.
Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.
import numpy as np
from glob import glob
# load filenames for human and dog images
human_files = np.array(glob("lfw/*/*"))
dog_files = np.array(glob("dogImages/*/*/*"))
print(dog_files[3])
# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
Step 1: Detect Humans¶
In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.
OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[0])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
Write a Human Face Detector¶
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
(IMPLEMENTATION) Assess the Human Face Detector¶
Question 1: Use the code cell below to test the performance of the face_detector function.
- What percentage of the first 100 images in
human_fileshave a detected human face? - What percentage of the first 100 images in
dog_fileshave a detected human face?
Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer: (You can print out your results and/or write your percentages in this cell)
from tqdm import tqdm
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
correct_faces = 0;
for human_path in human_files_short:
if face_detector(human_path) == True:
correct_faces=correct_faces+1
# print ("No of correct faces: "+ str(correct_faces))
print ("The procentage of human faces detected in images with humans is : "+str(correct_faces)+"%")
#-#-# Do NOT modify the code above this line. #-#-#
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
wrong_faces = 0;
for dog_path in dog_files_short:
if face_detector(dog_path) == True:
wrong_faces=wrong_faces+1
print ("The procentage of human faces detected in images with dogs is : "+str(wrong_faces)+"%")
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
Step 2: Detect Dogs¶
In this section, we use a pre-trained model to detect dogs in images.
Obtain Pre-trained VGG-16 Model¶
The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.
import torch
import numpy as np
import torchvision.models as models
# define VGG16 model
VGG16 = models.vgg16(pretrained=True)
# check if CUDA is available
use_cuda = torch.cuda.is_available()
# move model to GPU if CUDA is available
if use_cuda:
VGG16 = VGG16.cuda()
print("Train on cuda!")
Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.
(IMPLEMENTATION) Making Predictions with a Pre-trained Model¶
In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.
Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.
from PIL import Image
import torchvision.transforms as transforms
import requests
# Set PIL to be tolerant of image files that are truncated.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
MG_URL = 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg'
# Class labels used when training VGG as json, courtesy of the 'Example code' link above.
LABELS_URL = 'https://s3.amazonaws.com/outcome-blog/imagenet/labels.json'
# Let's get our class labels.
response = requests.get(LABELS_URL) # Make an HTTP GET request and store the response.
labels = {int(key): value for key, value in response.json().items()}
def VGG16_predict(img_path):
'''
Use pre-trained VGG-16 model to obtain index corresponding to
predicted ImageNet class for image at specified path
Args:
img_path: path to an image
Returns:
Index corresponding to VGG-16 model's prediction
'''
image = Image.open(img_path).convert('RGB')
in_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
image = in_transform(image)[:3,:,:].unsqueeze(0)
image.cpu()
#image.to('cuda')
prediction = VGG16(image)
prediction = prediction.data.numpy().argmax() # Our prediction will be the index of the class label with the largest value.
#print (prediction,labels[prediction])
## TODO: Complete the function.
## Load and pre-process an image from the given img_path
## Return the *index* of the predicted class for that image
return prediction # predicted class index
(IMPLEMENTATION) Write a Dog Detector¶
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).
Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
## TODO: Complete the function.
class_no = VGG16_predict(img_path)
if 151<= class_no <=268:
#print ('{} ,'.format(class_no))
return True # true/false
else:
return False
(IMPLEMENTATION) Assess the Dog Detector¶
Question 2: Use the code cell below to test the performance of your dog_detector function.
- What percentage of the images in
human_files_shorthave a detected dog? - What percentage of the images in
dog_files_shorthave a detected dog?
Answer:
The procentage of dogs detected in images with humans is : 0%.
The procentage of dogs detected in images with dogs is : 93%
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
dogs_detected = 0;
total_passed =0;
VGG16.cpu()
for dog_path in dog_files_short:
total_passed=total_passed+1
if dog_detector(dog_path) == True:
dogs_detected=dogs_detected+1
if(total_passed % 10 == 0):
print("Dogs detected so far ... {} out of {}".format(dogs_detected,total_passed))
else:
print("His fluflyness is not a dog, according to VGG16 :(")
img = cv2.imread(dog_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(cv_rgb)
plt.show()
#print ('No of dogs decected {}'.format(dogs_detected))
print ("The procentage of dogs detected in images with dogs is : "+str(dogs_detected)+"%")
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
dogs_detected = 0;
total_passed =0;
for human_path in human_files_short:
total_passed=total_passed+1
if dog_detector(human_path) == True:
print("Aperently, this human looks like a dog for vgg16 :(")
img = cv2.imread(dog_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(cv_rgb)
plt.show()
dogs_detected=dogs_detected+1
if(total_passed % 10 == 0):
print("Dogs detected so far ... {} out of {} ".format(dogs_detected,total_passed))
#print ('No of dogs decected {}'.format(dogs_detected))
print ("The procentage of dogs detected in images with humans is : "+str(dogs_detected)+"%")
We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)¶
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
 |
 |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
 |
 |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
 |
 |
 |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset¶
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!
import os
from torchvision import datasets
### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
data_dir = 'dogImages/'
train_dir = os.path.join(data_dir, 'train/')
test_dir = os.path.join(data_dir, 'test/')
valid_dir = os.path.join(data_dir, 'valid/')
data_transform = transforms.Compose([
transforms.RandomHorizontalFlip(), # randomly flip and rotate
transforms.RandomRotation(25),
transforms.Resize(225),
transforms.CenterCrop(224),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
in_transform = transforms.Compose([
transforms.Resize(225),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
train_data = datasets.ImageFolder(train_dir, transform=data_transform)
test_data = datasets.ImageFolder(test_dir, transform=in_transform)
valid_data = datasets.ImageFolder(valid_dir, transform=in_transform)
batch_size = 20
num_workers=0
# prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
loaders_scratch = {}
loaders_scratch['train']=train_loader
loaders_scratch['test']=test_loader
loaders_scratch['valid']=valid_loader
print('Num training images: ', len(train_data))
print('Num test images: ', len(test_data))
print('Num valid images: ', len(valid_data))
count=0;
classes=[]
entries = os.listdir(train_dir)
for x in entries:
result = ''.join(i for i in x if not i.isdigit()).replace("_"," ").replace(".","")
classes.append(result)
#print (result)
#print (classes)
print ("We have in total {} different breeds of dogs".format(len(classes)))
# obtain one batch of training images
dataiter = iter(valid_loader)
images, labels = dataiter.next()
images = images.numpy() # convert images to numpy for display
# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(20):
ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])
plt.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx]])
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# convolutional layer (sees 224x224x3 image tensor)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
# convolutional layer (sees 112x112x16 tensor)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
# convolutional layer (sees 56x56x32 tensor)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
# max pooling layer
self.pool = nn.MaxPool2d(2, 2)
# linear layer (64 * 28 * 28 -> 500)
self.fc1 = nn.Linear(64 * 28 * 28, 500)
# linear layer (500 -> 10)
self.fc2 = nn.Linear(500, 133)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
# add sequence of convolutional and max pooling layers
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
# flatten image input
x = x.view(-1, 64 * 28 * 28)
# add dropout layer
x = self.dropout(x)
# add 1st hidden layer, with relu activation function
x = F.relu(self.fc1(x))
# add dropout layer
x = self.dropout(x)
# add 2nd hidden layer, with relu activation function
x = self.fc2(x)
return x
# create a complete CNN
model_scratch = Net()
print(model_scratch)
#-#-# You do NOT have to modify the code below this line. #-#-#
# instantiate the CNN
# move tensors to GPU if CUDA is available
if use_cuda:
model_scratch.cuda()
Question 3: Describe your chosen procedure for preprocessing the data.
- How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
- Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?
Answer:
How does your code resize the images (by cropping, stretching, etc.)? What size did you pick for the input tensor, and why?
The images provided in the data set have different shapes and sizes. I decided to resize them at 225x225 pixels and then to crop them at 224x224. I have tried with smaller input images like 32x32 but i guess after so many convolutions not too many features remained, therefore my network stopped converging. With the input of 224x224 and some randomness added to the flip and rotation the network was able to not overfit on the data and also it had a accuracy of around 11%.
Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc.)? If not, why not?
Yes. Because if i did not augment the dataset the network would overfit on the provided data and not be able to generalize very well on the validation data.
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. Use the template in the code cell below.
import torch.nn as nn
model_scratch = Net()
#-#-# You do NOT have to modify the code below this line. #-#-#
# instantiate the CNN
model_scratch = Net()
# move tensors to GPU if CUDA is available
if use_cuda:
model_scratch.cuda()
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.
Answer:
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step
As i described on the question 3, a small input image (32x32 pixels) was not enough. Therefore, i decided to pass a bigger image in on the output. I picked only 3 layers do to the time constrain needed to train the network. Multiple convolutional layers would identify more subtle features and could generalize a little bit better but also the training time would increase. Therefore, i decided that 3 layers would be enough with two layers of a fully connected network. I've tried a fully connected network with 3 layers but did not improved the overall performance of the network. I believe that adding more convolutional layers would help improving the network performance
(IMPLEMENTATION) Specify Loss Function and Optimizer¶
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.
import torch.optim as optim
### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()
### TODO: select optimizer
optimizer_scratch = optim.SGD(model_scratch.parameters(), lr=0.01)
if use_cuda == True:
print("Training with CUDA!")
########################################################
#comment this if you did not train your network before!#
########################################################
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
(IMPLEMENTATION) Train and Validate the Model¶
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.
# the following import is required for training to be robust to truncated images
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf
for epoch in range(1, n_epochs+1):
# initialize variables to monitor training and validation loss
train_loss = 0.0
valid_loss = 0.0
###################
# train the model #
###################
model.train()
for batch_idx, (data, target) in enumerate(loaders['train']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
#print ("Size data: {} size trarget: {}".format(len(data),len(target)))
#print ("Size data: {} size trarget: {}".format(data.shape,target.shape))
#print(target)
## find the loss and update the model parameters accordingly
## record the average training loss, using something like
##
#data, target = data.cpu(), target.cpu()
optimizer.zero_grad()
output = model(data)
#print ("Size output: {} size trarget: {}".format(output.shape,target.shape))
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
######################
# validate the model #
######################
model.eval()
for batch_idx, (data, target) in enumerate(loaders['valid']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
#print("Dimension for data: {}, dimension for target{}".format(len(data),len(target)))
## update the average validation loss
output = model(data)
loss = criterion(output, target)
valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
# print training/validation statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch,
train_loss,
valid_loss
))
## TODO: save the model if validation loss has decreased
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
valid_loss))
torch.save(model.state_dict(), save_path)
valid_loss_min = valid_loss
# return trained model
return model
# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
# train the model
#model_scratch = train(100, loaders_scratch, model_scratch, optimizer_scratch,
# criterion_scratch, use_cuda, 'model_scratch.pt')
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
def test(loaders, model, criterion, use_cuda):
# monitor test loss and accuracy
test_loss = 0.
correct = 0.
total = 0.
model.eval()
for batch_idx, (data, target) in enumerate(loaders['test']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
total += data.size(0)
print('Test Loss: {:.6f}\n'.format(test_loss))
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
100. * correct / total, correct, total))
# call test function
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)¶
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset¶
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).
If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.
batch_size = 32
num_workers=0
loaders_transfer = loaders_scratch
(IMPLEMENTATION) Model Architecture¶
Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.
import torchvision.models as models
import torch.nn as nn
## TODO: Specify model architecture
model_transfer = models.vgg16(pretrained=True)
for param in model_transfer.features.parameters():
param.requires_grad = False
# print out the model structure
print(model_transfer)
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Here i decided just to change the final layer. I think that a fully connected network with 3 layers is enough to extract enough information for the classification task. The vgg16 is trained to identify 1000 classes therefore is very powerful at recognizing different features/patterns in an image. Because our classes are similar (dogs) we need a network that can identify small features that described a certain bread. Due to its depth vgg16 is a pretty good network to extract those features.
n_inputs = model_transfer.classifier[6].in_features
print(n_inputs,len(classes))
last_layer = nn.Linear(n_inputs, len(classes))
model_transfer.classifier[6]=last_layer
print(model_transfer.classifier[6].out_features)
print(model_transfer)
if use_cuda:
model_transfer = model_transfer.cuda()
print("Train on CUDA!")
Answer:
(IMPLEMENTATION) Specify Loss Function and Optimizer¶
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.
criterion_transfer = nn.CrossEntropyLoss()
optimizer_transfer = optim.SGD(model_transfer.classifier.parameters(), lr=0.001)
(IMPLEMENTATION) Train and Validate the Model¶
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.
# train the model
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
n_epochs=50
#model_transfer = train(n_epochs, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, 'model_transfer.pt')
# load the model that got the best validation accuracy (uncomment the line below)
#model_transfer.load_state_dict(torch.load('model_transfer.pt'))
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
(IMPLEMENTATION) Predict Dog Breed with the Model¶
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
# list of class names by index, i.e. a name can be accessed like class_names[0]
count=0;
classes=[]
entries = os.listdir(train_dir)
for x in entries:
result = ''.join(i for i in x if not i.isdigit()).replace("_"," ").replace(".","")
classes.append(result)
#print (result)
#print (classes)
print ("We have in total {} different breeds of dogs".format(len(classes)))
def predict_breed_transfer(img_path):
image = Image.open(img_path).convert('RGB')
in_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor()])
image = in_transform(image)[:3,:,:].unsqueeze(0)
image=image.cuda()
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
model_transfer.cuda()
prediction = model_transfer(image)
prediction = prediction.cpu()
prediction = prediction.data.numpy().argmax()
# load the image and return the predicted breed
return classes[prediction]
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
img_path ='D:\Dropbox\Roboti\Media\Photos\Team Members Profile Pics\John\cristian_dobre.jpg'
image = Image.open(img_path).convert('RGB')
#cv_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(image)
plt.show()
print("Jon looks like a: " + str(predict_breed_transfer(img_path)))
%matplotlib inline
img_path ='./dogImages/valid/056.Dachshund/Dachshund_03926.jpg'
image = Image.open(img_path).convert('RGB')
#cv_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(image)
plt.show()
print("This dog is a Dachshund and the predicted breed is: " + str(predict_breed_transfer(img_path)))
Step 5: Write your Algorithm¶
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
- if a dog is detected in the image, return the predicted breed.
- if a human is detected in the image, return the resembling dog breed.
- if neither is detected in the image, provide output that indicates an error.
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
(IMPLEMENTATION) Write your Algorithm¶
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
VGG16 = VGG16.cpu()
def run_app(img_path):
if dog_detector(img_path) is True:
print("Hello your marvelous fluflynes!\nYour breed seems to be: " + str(predict_breed_transfer(img_path)))
elif face_detector(img_path) > 0:
print("Hello, human! You look like a "+ str(predict_breed_transfer(img_path))+ " today")
else:
print("Error. The humans and dogs are outside. They have a life you know...")
img = Image.open(img_path)
plt.imshow(img)
plt.show()
import os
for img_file in os.listdir('./images'):
img_path = os.path.join('./images', img_file)
run_app(img_path)
Step 6: Test Your Algorithm¶
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
(IMPLEMENTATION) Test Your Algorithm on Sample Images!¶
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: (Three possible points for improvement)
First point would be to improve the human detection. The HAAR cascade has some problems identifying the human in the picture with DALI, due to his mustache :). HAAR cascade provides best results when applied to images of frontal faces. In order to improve this detection, a modified HAAR shoud be used trained on faces taken from the profile. Moreover, a solution like YOLO could help with this detection.
The second point would be to improve the detection of the dog bread. Now the network has a 75% accuracy. A better accuracy can be obtained wiht a larger data set. Also, a different network with more layers could be used. This will help us identifiy more distinct features for every bread.
Moreover, the optimizer for the network can be experimented with, given that ADAM for example is considerd to provide better results.
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
import os
for img_file in os.listdir('./Final Test Images'):
img_path = os.path.join('./Final Test Images', img_file)
run_app(img_path)
## suggested code, below
#for file in np.hstack((human_files[:3], dog_files[:3])):
# run_app(file)
